Risk Pooling And Regulation: Policy And Reality In ... - Health Affairs

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M a r k e t Watc h Risk Pooling And Regulation: Policy And Reality In Today’s Individual Health Insurance Market The individual market’s main failing is not how it treats high risks but how it treats all risks. by Mark V. Pauly and Bradley Herring ABSTRACT: Analysis of new data on the relationship between and premiums and coverage in the individual insurance market and health risk shows that actual premiums paid for individual insurance are much less than proportional to risk, and risk levels have a small effect on obtaining coverage. States limiting risk rating in individual insurance display lower premiums for high risks than other states, but such rate regulation leads to an increase in the total number of uninsured people. The effect on risk pooling is small because of the large amount of risk pooling in unregulated individual insurance. [Health Affairs 26, no. 3 (2007): 770–779; 10.1377/hlthaff.26.3.770]

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m e r i c a n s s h o u l d a l l have health insurance. But Americans differ in risk, and that difference potentially affects both the value they attach to health insurance and the premiums they are charged for it. How private insurance treats customers at different levels of risk is often thought to be most pronounced in the smallest segment of the health insurance market: the market for individual (sometimes called nongroup) insurance. Purchasers in this market are more heterogeneous than those for other kinds of health insurance—in their characteristics and risk levels. Not surprisingly, they then face explicit variation in premiums that depends on some of these characteristics— generally the ones thought to be predictive of expected benefits—of which health risk is probably the most important. The overall variation in premiums paid, prior research tells us, depends on the amount

and type of insurance chosen, the effort or luck that attends the buyer’s search process, and the buyer’s characteristics thought by insurers to be correlated with risk.1 However, premiums for individual insurance are also affected by policy provisions guaranteeing the renewability of coverage at rating-class average premiums, independent of any changes in the insured person’s risk. Individual insurance contracts generally do not permit “renewal underwriting.” How much linking of premiums to risk actually exists cannot be settled by conjecture or anecdote; it requires empirical evidence. The individual market is extraordinarily untidy, variegated, and malleable. It is this way precisely because it is the wide-open “residual market” that has the task of picking up those who do not obtain employer-group coverage. Some have questioned whether its use should be encouraged for these purposes. Some

Mark Pauly ([email protected]) is the Bendheim Professor in the Health Care Systems Department of the Wharton School, University of Pennsylvania, in Philadelphia. Bradley Herring is a professor in the Department of Health Policy and Management at Emory University, in Atlanta, Georgia.

770 DOI 10.1377/hlthaff.26.3.770 ©2007 Project HOPE–The People-to-People Health Foundation, Inc.

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policymakers and regulators would agree with a frustrated buyer quoted in a recent Wall Street Journal story: “If you have a job with health coverage, then you get health coverage. If you don’t, you’re simply out of luck.”2 There would even be more agreement with this article’s conclusion—that the individual market “has a big problem: sick people often cannot get insurance or if they can, it’s prohibitively expensive.” “Often” is not a useful number, but knowing what proportion of high risks actually pay high premiums or fail to have coverage in this market, and how this compares with the proportion of people at other risk levels and in other insurance markets, would be useful. How serious are the problems, are they worse than in group markets, and do some arrangements in the individual market work better than others? The individual market covers a small fraction of those who are privately insured, and it attracts only about a quarter of those without group coverage.3 Those left uninsured fall through the cracks even in this market of last resort. The individual market has assumed a more prominent place than its current 6 percent share of the privately insured population suggests. It is sometime proposed as a target for tax credits. Both greater use of and reform in the individual market are at the center of the recently passed Massachusetts plan to cover the uninsured. Just as there is much variation among the people who could use the individual market and actual use of it in reality, so there is much variation in policy analysts’ and policymakers’ perceptions about whether this market should be encouraged and improved or disparaged and discouraged. In this paper we provide evidence on how this market functions. The data describe insurance pricing and purchasing by risk level.

Risk Variation And The Purchase Of Insurance: What Might We Expect? Before turning to the data, we discuss what one might expect to see in such a market. We begin with the benchmark economic model of rational insurance purchasing. Consider a set

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of people who are not poor, and suppose that individual insurance were to be offered to them in a large variety of forms and premiums. The conventional wisdom, based on anecdotes and elementary economic thinking, is that (1) profit-seeking insurers will charge high-risk people premiums proportional to that high risk, and (2) many such people will respond by being less likely to buy. More sophisticated economic theory does not offer unequivocal support for either proposition. While the higher risks will pay higher premiums, they will expect on average to collect more benefits. For middle-income people, there need be no substantial negative impact of risk rating on the overall proportion of a population obtaining coverage; as long as the administrative cost is not too large, high risks need not be “priced out” of coverage that pays for expenses they would incur in any case. But what about those lower-income people who are unusually high risks? Isn’t it likely that even moderately well off, higher-risk households would be unable to “afford” higher premiums? There is no precise or useful definition of affordability available.4 The availability of charity care to the uninsured might provide a viable, although certainly less attractive, alternative.5 It is therefore an empirical matter whether high premiums for high risks discourage insurance coverage and whether any such effects increase as income falls.

What The Data On Premiums Show Since we cannot settle things with a priori reasoning, we need to see how individual insurance markets really work. Typically people on both sides of the political divide have tried to study this market by looking at hypotheticals: hypothetical prices for hypothetical insurance purchases by hypothetical customers. For example, they look at the premiums that insurers “quote,” without regard to whether or not the insurer will actually sell coverage at that premium upon receipt of an underwriting report or whether buyers will pay that premium.6 Or they look at what buyers with various health histories were told when they first called an insurer, without re-

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gard to the benefits of shopping around or negotiating (sometimes by the consumer but more frequently by an insurance broker).7 And they almost always look at brand-new customers unknown to the insurer and the potential premium at first issue; they do not look specifically at customers who have renewed with the same insurer. To avoid these kinds of noisy and hypothetical measures, we examined data from several large samples of people who might be buyers of individual insurance, and we looked at two aspects of their actual behavior: whether or not they obtained coverage and, if they did, what premium they paid.

How Things Used To Be, And How They May Have Changed n Earlier findings. The work reported here should be contrasted with our prior work studying individual insurance markets in the late 1980s.8 That work found that premiums actually paid were far away from the model of proportional risk rating. We found that although premiums increased with expected expenses, they did so much less than proportionately. Moreover, the only risk variables that predicted higher premiums consistently were age, sex, and location; given these variables, those who had higher expected expenses (associated usually with chronic conditions) could not be shown to pay more than those who did not. We also looked at whether risk affected having coverage. We found that the likelihood of having coverage was no smaller for the higher risks than for the lower risks, controlling for income, age, and sex. Indeed, controlling for income, younger people (despite risks and premiums much below average) were less likely than people in their fifties (who paid much higher premiums) to buy coverage.9 There was a great amount of de facto “risk pooling”—in the sense that people with different expected loss experiences paid similar individual insurance premiums. The amount of averaging appears at first to be so great as to be hard to square with rational profit-maximizing behavior by informed insurers. Either highrisk consumers were hoodwinking insurers, or

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something else was going on. We did identify one thing that affected the relationship of risk to premiums: guaranteed renewability.10 Even though not then generally required by regulation, guaranteed renewability was a common policy provision. If most people with insurance with this provision had initially bought individual coverage when they were relatively low risks but then stuck with it as their risk levels increased as a result of chronic conditions, their later premiums would not have varied with person-specific risk. Since average risk (and premium) rises with age, making such a promise implies that premiums for younger people will be “front loaded,” containing an extra charge to cover the future loss of those who become high risks, which insurers then hold as reserves; this phenomenon would be highly consistent with the finding, already reported, that premiums rise much less than proportionately with expected expenses as a result of age. Finally, it also means that the overall or average extent to which premiums vary with risk reflects a mix of potentially strong risk rating at first issue and no risk rating upon renewal. n Current study. This time we looked at three different and newer data sets, each larger than the 1987 National Medical Expenditure Survey (NMES) data we examined earlier: the Medical Expenditure Panel Survey (MEPS) for 1996–2002, the Community Tracking Study’s Household Survey (CTS-HS) for 1999 and 2001, and the National Health Interview Survey (NHIS) for 1999–2004. One might expect some differences from the earlier results. Premiums were much higher in the later study periods, so insurers might have been less willing than they were earlier to tolerate higher risks. However, high risks should have been more eager to get coverage. Another change is that more states were regulating how much premiums for first-time individual coverage buyers can vary with risk (through required rating bands or strict community rating). Insurers also might have had new technology to identify risks and new incentives to avoid high risks when regulation

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permits. Finally, the Health Insurance Portability and Accountability Act (HIPAA) required all states to enforce some type of guaranteed-renewability provisions, but actual state regulation seemed to vary.11 We therefore wanted to see what was happening with more recent data.

Some Technicalities: What Is Risk? What Are Its Components? In this new work we followed our earlier procedure in measuring risk by expected medical expenses. We developed a multiple regression model, applied to the expenses of the insured population, to predict a person’s risk based on age, sex, and the presence of multiple chronic conditions. The factors that contribute on average to high levels of health spending are factors that (we assume) both consumers and insurers would take as indicators of high risk. Moreover, the quantitative importance of any such risk factor is to be measured by its contribution to expected expenses. The first two columns of Exhibit 1 show both the distribution of actual health spending and the estimated risk distribution as predicted by our model for the sample of candidates for individual coverage purchase. (Note that the distribution of risk is different from

the distribution of actual expenses; there is less variation in the former than in the latter. To use the distribution of actual expenses to provide evidence for the “problem” of risk variation, as many analysts do, is thus potentially misleading.)12 The last two columns show the variation in risk attributable to age and sex alone and then the variation in relative risk, controlling for age and sex, attributable to health conditions. This so-called index of condition-related expense is the ratio of expected expense using all risk characteristics over expected expense using only age and sex. We wanted to examine these distinct components of risk separately in our analysis because of the easier observability of some of the risk characteristics relative to others. Age and sex are easily observable characteristics, while the onset of a chronic disease (and the above-average medical costs for subsequent years) is uncertain and might be hard for insurers to detect.13

What The More Recent Data On Premiums Show n Expected medical costs and premiums. Exhibit 2 illustrates our main findings. It compares the actual medical costs, the expected medical costs, and individual insurance premiums for single-person policies, splitting

EXHIBIT 1 Distribution Of Actual And Expected Health Expenses Of Potentially Privately Insured Individuals, In 2002 Dollars, 1996–2002 Expected expense based only on age and sex ($)

Index of condition-related expected expense

Actual expense ($)

Expected expense ($)

Mean 99th percentile 95th percentile 90th percentile

1,840 22,349 7,954 4,263

1,817 10,351 4,064 3,079

1,759 4,710 4,110 3,359

1.01 3.95 1.81 1.32

75th percentile 50th percentile 25th percentile 10th percentile

1,358 367 56 0

2,086 1,461 750 546

2,345 1,516 800 577

0.95 0.88 0.78 0.72

82,711

90,756

90,756

N

90,756

SOURCE: Data from the Medical Expenditure Panel Survey, 1996–2002. Expected expense is derived from the authors’ calculations.


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EXHIBIT 2 Expected Expense And Individual Insurance Premiums For Single-Person Coverage, Selected Years 1996–2004

Data source

Average actual expense ($)

Average expected expense ($)

Average individual insurance premium ($)

MEPS data (N = 871) Low expected expense High expected expense

1,005 3,330

1,074 4,253

2,178 3,308

CTS-HS data (N = 1,459) Low expected expense High expected expense

–a –a

1,010 4,924

1,539 2,430

NHIS data (N = 2,452) Low expected expense High expected expense

–a –a

1,361 3,525

2,208 3,777

SOURCES: Data from the Medical Expenditure Panel Survey (1996–2002); Community Tracking Study Household Survey (1999 and 2001); and National Health Interview Survey (1999–2004). Expected expense is derived from the authors’ calculations. NOTE: Low and high expected expense are defined as above and below the median expected expense for each sample. a Not available.

the sample by those in either the lower or upper half of risk (that is, expected expense). As the MEPS data show, the predicted high risks (above the median) had both high expected expenses (before the fact) and high actual expenses (after the fact); they were roughly four times higher compared with the bottom half. But the premiums that higher-risk people actually paid were only, on average, about 1.6 times those of lower-risk people. Although there may be some variation with risk in the extent of coverage for those with insurance, even the direction of such variation is uncertain and need not be related to whether or not some insurance was purchased. At least half of their higher expected expense appears to be pooled, even in the individual market. The average premium reflects the experience of both new purchasers and those who renewed coverage. Could it be that we did not observe proportionately higher premiums charged to higher risks because insurers were using underwriting to deny the seriously ill among them coverage or (what is really the same thing) quoting them so high a premium that they decided not to buy? Then they would not be included in our sample of transactions. n Prevalence of chronic conditions and

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premium costs. Exhibit 3 shows the condition-related component of our measure of expected expense, broken down by income level and current insurance status: employmentbased insurance, individual insurance, uninsured, and Medicaid. Comparing the measure of condition-related expected expense between high-income people with individual insurance (0.959) and those who were uninsured (0.997) does provide some support for a modest number of high risks’ having been pushed from the individual market to the ranks of the uninsured, but the comparisons for the low-income people do not imply that similar displacement occurred for them. The overall results do suggest that the average health status for those with private insurance (whether group or individual) did not differ markedly from that of people who were uninsured (although there may well be more variation among the uninsured). Those covered by Medicaid, by contrast, had much higher risk and much higher condition-related expenses, as one would expect. In our more rigorous analyses of the newer data, we nevertheless made a statistical adjustment to account for the possibility that some of the higher risks in the “potential” individual

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EXHIBIT 3 Prevalence Of Chronic Health Conditions By Insurance Status And Income, 1996– 2002 Index of condition-related expected expense

Low income High income

Employment-based insurance

Individual insurance

Uninsured

Medicaid

1.045 1.003

1.025 0.959

1.023 0.997

1.183 –a

SOURCE: Data from the Medical Expenditure Panel Survey, 1996–2002. Expected expenses are derived from the authors’ calculations. NOTE: Low and high income are defined as below and above 300 percent of the federal poverty level, respectively. a Not applicable.

market were denied coverage.14 Using a “selection correction” regression model approach for the CTS-HS and NHIS samples, we found that higher health risk was significantly related to higher premiums overall. While premiums rose with risk, as indicated, each of these measures of the relationship between premiums and risk was well below unity: Premiums were definitely far from proportional to risk, so there was a substantial amount of risk pooling present. The average increase in premium due to increases in the demographic component was 32.8–48.0 percent (p < 0.01), similar to what it was in our earlier work. The health conditions component, although statistically significant, had in contrast a much lower relationship, 11.5–15.5 percent (p < 0.01) in these two data sets. Although in these data people of a given age and sex with chronic health conditions paid higher premiums than people without such conditions, individual insurance, through guaranteed renewability or some other device, pooled 84.5–88.5 percent of the risk as a result of the random effect of chronic conditions. We also examined the relationship between conditions and premiums for new insurance purchasers only; the relationship in this smaller subsam-ple was not statistically significant. To sum up, both more recent data sets show that even after HIPAA and some increase in state regulation, the average relationship of premiums paid to risk for the full sample is quite similar to the relationship measured ear-


lier. Premiums paid still increased with risk, but they still increased much less than proportionately. The most important risk factors in predicting higher premiums are the person’s age and sex; chronic conditions per se matter, but their effect is quite small relative to their effect on risk.

Effects Of Regulation A minority of states regulate, to various extents, the way in which insurers are allowed to vary premiums with risk or reject applicants based on risk. The strongest case (in New York) is full community rating, in which an insurer must charge the same premium for the same policy to everyone in a community. A few other states use “modified community rating,” which allows premiums to vary with age but not with health status. Some states put bands on the way premiums may vary with risk and place less stringent limits on underwriting practices. There is no bright line that perfectly distinguishes states that regulate rating of premiums from those that do not, because virtually every state can forbid premiums that are “arbitrary,” while the definition of arbitrariness is itself arbitrary. We therefore selected two groups: six states with the reputation of being the most aggressive regulators using either full or modified community rating along with guaranteed issue, versus thirty-four states with no rating regulations or guaranteed-issue laws.15 We compared how premiums (and in-

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surance coverage further below) varied with health condition–related risk, given age and sex, in these two groups of states. n Impact of regulation on premiums. The results in Exhibit 4 suggest that the small but positive effect of health conditions on premiums observed in the regression analysis described earlier existed (to a statistically significant extent) only in the unregulated states. We also found (in results not shown) that the higher premiums for sicker people in these unregulated states were largely confined to lowincome people. Apparently higher-income high-risk people in less regulated states either found lower premiums that they were willing to pay or were more likely to have previously obtained insurance with guaranteed renewability. n Effect of risk on obtaining coverage. We next looked at the relationship between risk and the probability of obtaining coverage. What, if anything, does the risk-premium relationship, and variation in it caused by regulation, do to the risk-coverage relationship? We assumed that the intent of regulation was to increase overall coverage; did it do so?16 We must specify who the potential buyers in the individual market are. Here we assumed that those who had no family member currently working at a firm, or who were not cov-

ered by employment-based insurance, were the candidates for individual insurance. Because of the high loading in individual insurance, however, the fraction of people in this set, at any risk level in any state, who actually bought coverage was relatively small. Did regulation then matter for the relationship between risk and coverage? Exhibit 5 shows the relative rates of obtaining individual insurance coverage for various percentiles of risk (measured by expected expense), split between those states with neither rating nor issue regulations versus those with both community rating and guaranteed issue; these relative rates were normalized to the average proportion insured in the sample. The effect of health risk on insurance coverage was negative and statistically significant in unregulated states, relative to an insignificant effect in heavily regulated states. However, the difference in the estimated relationship between risk and coverage between regulated and unregulated states was small. To be specific: In unregulated states, highrisk people (at the ninetieth percentile of the risk distribution, conditional on age and sex) were 91.5–92.9 percent as likely as an averagerisk person to obtain coverage; in regulated states, there was no difference in likelihood of coverage by risk level. Thus, regulation in-

EXHIBIT 4 Effect Of State Rating Regulations On The Percentage Change In Individual Insurance Premiums With Respect To A 100 Percent Change In Risk, Selected Years 1999–2004 States with no rating or issue regulations

States with community rating and guaranteed-issue regulations

Data source

Expected expense from age and sex


Expected expense from age and sex


CTS-HS data NHIS data

35.6%*** 46.5%***

7.4%** 10.8%**

54.5%*** 25.1%**

2.3% 6.9%

SOURCE: Estimates from B. Herring and M. Pauly, “The Effect of State Community Rating Regulations on Premiums and Coverage in the Individual Health Insurance Market,” NBER Working Paper no. 12504 (Cambridge, Mass.: National Bureau of Economic Research, August 2006), derived from a selection-correction model for individual insurance premiums. NOTES: CTS-HS is Community Tracking Study Household Survey (1999, 2001). NHIS is National Health Interview Survey (1999–2004). **p < 0.05 ***p < 0.01

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EXHIBIT 5 Effect of Rating Regulations On Overall Insurance Coverage: Relative Rates Of Insurance Coverage, Selected Years 1999–2004

Expected expense

States with no rating or issue regulations

States with community rating and guaranteed-issue regulations

CTS-HS data: average 95th percentile 90th percentile 75th percentile 50th percentile 25th percentile 10th percentile 5th percentile

1.000 0.899 0.929 0.974 1.017 1.027 1.045 1.056

0.940 0.984 0.970 0.951 0.932 0.929 0.921 0.917

NHIS data: average 95th percentile 90th percentile 75th percentile 50th percentile 25th percentile 10th percentile 5th percentile

1.000 0.882 0.915 1.006 1.014 1.020 1.029 1.044

0.926 0.930 0.929 0.926 0.926 0.925 0.925 0.925

SOURCE: Estimates from B. Herring and M. Pauly, “The Effect of State Community Rating Regulations on Premiums and Coverage in the Individual Health Insurance Market,” NBER Working Paper no. 12504 (Cambridge, Mass.: National Bureau of Economic Research, August 2006). NOTES: Relative rates of insurance coverage are normalized to the average rate of insurance coverage in states with low levels of rating regulations. The average difference between states with low and high rating regulations are not actual differences but are instead simulated differences. CTS-HS is Community Tracking Study Household Survey (1999, 2001). NHIS is National Health Interview Survey (1999–2004).

creased the relative likelihood of coverage for high risks by 7.1–8.5 percent. (Had we defined high-risk people as those with high expected expenses based both on age/sex and the presence of chronic conditions, the impact of regulation would have been much smaller, with the proportion varying by extent of regulation less than 2 percent.) However, this modest difference does not necessarily translate into the conclusion that regulation helps the uninsured overall. There are two edges to the community rating or premium averaging sword: To help higher risks buy coverage, it discourages lower risks from doing so. Thus, regulation might help only some of the uninsured by harming others. The last relationship to be investigated is therefore the following: How is premium regulation related to the total proportion (at all risk levels) who remained uninsured? That is, compared with unregulated states, did regula-


tion bring in more high risks than it discouraged low risks? We obviously could not simply compare the proportions uninsured in regulated and unregulated states, because many other factors, some unknown, influence insurance purchasing across states; it would be hard to tease out the effect of regulation alone. So we used our results of the effect of regulation on purchasing by different risks in a simulation analysis.17 As already noted, regulation increases the relative likelihood that higher risks will obtain coverage compared to lower risks. Since this change in mix in the insurance purchasing pool raises the average level of risk, competitive insurance markets will require average premiums to increase, relative to unregulated states (other things equal). This increase in premiums will then cause both high risks and low risks to drop coverage—the magnitude of which we can simulate using established esti-

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mates for the responsiveness of insurance purchasing to price. In a “first round” that did not yet incorporate the effect of increased premiums on coverage, we used the previous model to estimate how regulation would affect the mix of risks. For example, we estimated that regulation would increase the relative rates of insurance from 0.915–0.929 to 1.003–1.032 at the ninetieth percentile of risk and would reduce the relative rate of insurance from 1.029–1.045 to 0.999–0.980 at the tenth percentile of risk. The “second round” incorporated the effect of this shift in composition of insurance coverage by risk on the average premium. Using the results for the distribution of expected expense for those in the CTS-HS and NHIS data, we estimated that the shift in composition of insured people from lower to higher risks as a result of regulation would raise the average premium by approximately 12.0–14.8 percent. This in turn would result in a decrease in the percentage insured by 6.0–7.4 percent—using a consensus estimate of the price-elasticity of insurance.18 This calculation yielded the results shown in the right side of Exhibit 5, where the “baseline” average relative rate of insurance coverage is now 0.926–0.940 for states with community rating and guaranteed-issue regulations. That is, these regulations reduced the overall proportion of eligible people in the individual market with insurance by 6.0–7.4 percent.

Discussion What is striking about these estimates is not their precise magnitude, but rather the fact that their magnitude is small. The effects of rate regulation on relative probability of insurance purchasing in the individual insurance market were exceedingly modest, especially when compared with the overall shortfall of 74 percent of the eligible population in this market who did not buy insurance at all. The reason for such a small effect of regulation is, we believe, precisely because there was a very high level of risk pooling in this market even in the absence of regulation; there was not much more that regulation could (or, apparently,

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did) do. That the effect of rate regulation on overall coverage was also rather small (even if negative) probably helps to explain why informal cross-state analyses of those regulated states whose insurance markets have not collapsed failed to find much; there is too much turbulence to measure this little zephyr.19 The individual market’s main failing is not how it treats high risks but how it treats all risks: By charging them all high premiums relative to the value of benefits and by not being able to offer the generous tax subsidies available to group insurance, it performs poorly. Perhaps the development of ways to lower the administrative expenses for individual insurance across the board, rather than further tinkering with the structure of high premiums, would be the most helpful public policy. Here is an additional reason for this conclusion: Although regulation did slightly raise the number of high-risk low-income people who obtained coverage compared with unregulated risk rating, the overall proportion of lowincome people at all risk levels who were willing to buy individual coverage was so low (less than 10 percent) that in the end, very few people were helped. In contrast, even in relatively unregulated states, the individual market treated middle-income higher risks rather well. It pooled a very large fraction of total risk, and it got coverage to people who were higher risk by virtue of being older. Lowering administrative costs or creating high-risk pools, rather than rate regulation, would also avoid regulation-induced incentives to insurers to avoid money-losing high risks.

O

u r a na lys i s strongly suggests that insurance policymakers should be much less concerned about the “Achilles’ heel” of risk variation and more concerned about victory in the overall effort to make individual insurance more affordable to more people at all risk levels.

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This research was supported by a contract from the Office of the Assistant Secretary for Planning and Evaluation, U.S. Department of Health and Human Services. The opinions expressed are those of the authors alone.

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The search process for insurance is examined in M. Pauly, B. Herring, and D. Song, “Information Technology and Consumer Search for Health Insurance,” International Journal of the Economics of Business 13, no. 1 (2006): 45–63. S. Lueck, “Seeking Insurance, Individuals Face Many Obstacles,” Wall Street Journal, 31 May 2005. M.B. Buntin, M.S. Marquis, and J.M. Yegian, “The Role of the Individual Health Insurance Market and Prospects for Change,” Health Affairs 23, no. 6 (2004): 79–90. M.K. Bundorf and M.V. Pauly, “Is Health Insurance Affordable for the Uninsured?” Journal of Health Economics 25, no. 4 (2006): 650–673. B. Herring, “The Effect of the Availability of Charity Care to the Uninsured on the Demand for Private Health Insurance,” Journal of Health Economics 24, no. 2 (2005): 225–252. V. Patel, “Analysis of National Sales Data of Individual and Family Health Insurance,” June 2001, http://www.ehealthinsurance.com/ehealth insurance/eHealth2.pdf (accessed 9 August 2006). K. Pollitz, R. Sorian, and K. Thomas, How Accessible Is Individual Health Insurance for Consumers in Lessthan-Perfect Health? (Menlo Park, Calif.: Henry J. Kaiser Family Foundation, June 2001); and N. Turnbull and N. Kane, Insuring the Healthy or Insuring the Sick? The Dilemma of Regulating the Individual Health Insurance Market, Report no. 771 (New York: Commonwealth Fund, February 2005). M. Pauly and B. Herring, Pooling Health Insurance Risks (Washington: AEI Press, 1999). M. Pauly and L. Nichols, “The Nongroup Health Insurance Market: Short on Facts, Long on Opinions and Policy Disputes,” Health Affairs 21 (2002): w325–w344 (published online 23 October 2002; 10.1377/hlthaff.w2.325). M. Pauly, H. Kunreuther, and R. Hirth, “Guaranteed Renewability in Insurance,” Journal of Risk and Uncertainty 10, no. 2 (1995): 143–156; and B. Herring and M.V. Pauly, “Incentive-Compatible Guaranteed Renewable Health Insurance Premiums,” Journal of Health Economics 25, no. 3 (2006): 395–417. V. Patel and M. Pauly, “Guaranteed Renewability and the Problem of Risk Variation in Individual Health Insurance Markets,” Health Affairs 21


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(2002): w280–w289 (published online 28 August 2002; 10.1377/hlthaff.w2.280). M.L. Berk and A.C. Monheit, “The Concentration of Health Care Expenditures, Revisited,” Health Affairs 20, no. 2 (2001): 9–18. The risk measures in our data obviously do not include all of the information an insurer might have or use to determine risk. If insurers use other measures, the risk attributable to them might have a different relationship to premiums than the risk we measured. However, there is no reason to think that the relationship to the attributes that we measured is different. More detail regarding the methodology used to generate these results can be found in B. Herring and M. Pauly, “The Effect of State Community Rating Regulations on Premiums and Coverage in the Individual Health Insurance Market,” NBER Working Paper no. 12504 (Cambridge, Mass.: National Bureau of Economic Research, August 2006). States with community rating and guaranteedissue regulations during this time period were ME, MA, NH, NJ, NY, and VT. States with no rating regulations or guaranteed issue were AL, AK, AZ, AR, CA, CO, CT, DE, FL, GA, HI, IL, IN, KS, LA, MD, MI, MN, MS, MO, MT, NE, NV, NC, OK, PA, SC, TN, TX, VA, DC, WV, WI, and WY. A reviewer suggested that regulators might only be concerned about coverage of higher risks and unconcerned about lower-risk people becoming uninsured. This is not the assumption we made. This is a simplified version of the well-accepted model developed by K. Subramanian et al., “Estimating Adverse Selection Costs from Genetic Testing for Breast and Ovarian Cancer: The Case of Life Insurance,” Journal of Risk and Insurance 66, no. 4 (1999): 531–550. S. Glied, D.K. Remler, and J.G. Zivin, “Inside the Sausage Factory: Improving Estimates of the Effects of Health Insurance Expansion Proposals,” Milbank Quarterly 80, no. 4 (2002): 603–635. T. Buchmueller and J. DiNardo, “Did Community Rating Induce an Adverse Selection Death Spiral? Evidence from New York, Pennsylvania, and Connecticut,” American Economic Review 92, no. 1 (2002): 280–294; and D. Chollet and R. Paul, Community Rating: Issues and Experience (Washington: Alpha Center, December 1994).

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